Ghislain Fourny. Big Data 6. Massive Parallel Processing (MapReduce)
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1 Ghislain Fourny Big Data 6. Massive Parallel Processing (MapReduce)
2 So far, we have... Storage as file system (HDFS) 13
3 So far, we have... Storage as tables (HBase) Storage as file system (HDFS) 14
4 Data is only useful if we can query it Querying Storage as tables (HBase) Storage as file system (HDFS) 15
5 ... in parallel Querying Storage as tables (HBase) Storage as file system (HDFS) 16
6 Data Processing Input data 17
7 Data Processing Input data Query 18
8 Data Processing Input data Query Output data 19
9 MapReduce 20
10 Data Processing: data comes in chunks Query 21
11 Data Processing: the ideal case Query Query Query Query Query Query Query Query 22
12 Data Processing: the worst case 23
13 Data Processing: the typical case 24
14 Data Processing: Map here... 25
15 Data Processing:... and shuffle there 26
16 A common and useful sub-case: MapReduce Input data 27
17 A common and useful sub-case: MapReduce Input data Map Map Map Map Map Map Map Map 28
18 A common and useful sub-case: MapReduce Input data Map Map Map Map Map Map Map Map Shuffle 29
19 A common and useful sub-case: MapReduce Input data Map Map Map Map Map Map Map Map Shuffle Reduce Reduce Reduce Reduce Reduce Reduce Reduce Reduce 30
20 A common and useful sub-case: MapReduce Input data Map Map Map Map Map Map Map Map Shuffle Reduce Reduce Reduce Reduce Reduce Reduce Reduce Reduce Output data 31
21 Data Processing: Data Model Input data Map Map Map Map Map Map Map Map Intermediate data (shuffled) Reduce Reduce Reduce Reduce Reduce Reduce Reduce Reduce Output data 32
22 Data Processing: Data Shape Key- pairs Map Map Map Map Map Map Map Map Key- pairs Reduce Reduce Reduce Reduce Reduce Reduce Reduce Reduce Key- pairs 33
23 Data Processing: Data Types key type 1 -> type 1 Map Map Map Map Map Map Map Map key type I -> type I Reduce Reduce Reduce Reduce Reduce Reduce Reduce Reduce key type A -> type A 34
24 Data Processing: Most often key type 1 -> type 1 Map Map Map Map Map Map Map Map key type A -> type A Reduce Reduce Reduce Reduce Reduce Reduce Reduce Reduce key type A -> type A 35
25 Splitting 36
26 Splitting Split 37
27 Splitting key 1 Split key 2 key 3 key 4 38
28 Mapping function key 1 39
29 Mapping function key 1 Map 40
30 Mapping function key 1 Map key I key II 41
31 Mapping function... in parallel key 1 Map key I key II 42
32 Mapping function... in parallel key 1 Map key I key II key 2 Map key I key III 43
33 Mapping function... in parallel key 1 Map key I key II key 2 Map key I key III key 3 Map key II key III 44
34 Put it all together key I key II key I key III key II key III 45
35 Put it all together key I key II key I key III key II key III 46
36 Put it all together key I key II key I key II key I key I key III key III key II key II key III key III 47
37 Sort by key key I key II key I key III key III key II key I 48
38 Sort by key key I key I key II key I key I key I key III key II key III key II key II key III key I key III 49
39 Partition key I key I key I key II key II key III key III 50
40 Partition key I key I key I key II key II key III key III 51
41 Partition key I key I key I key I key I key I key II key II key II key II key III key III key III key III 52
42 Reduce function key I key I key I 53
43 Reduce function key I key I key I Reduce 54
44 Reduce function key I key I key I Reduce key A 55
45 Reduce function (with identical key sets) key A key A key A A B C Reduce key A 56
46 Reduce function (most generic) key I key I key I Reduce key A ( key B ) More is fine, but uncommon 57
47 Reduce function... in parallel key I key I key I Reduce key A 58
48 Reduce function... in parallel key I key I key I Reduce key A key II key II Reduce key B 59
49 Reduce function... in parallel key I key I key I Reduce key A key II key II Reduce key B key III key III Reduce key C 60
50 Overall 61
51 Overall Map 62
52 Overall Map 63
53 Overall Map Sort 64
54 Overall Map Sort 65
55 Overall Map Sort Partition 66
56 Overall Map Sort Partition 67
57 Overall Map Sort Partition Reduce 68
58 Overall Map Sort Partition Reduce 69
59 Input/Output formats 70
60 Input and output formats 71
61 Input and output formats From/to tables 72
62 Input and output formats From/to tables From/to files 73
63 Formats: tabular 74
64 Formats: tabular RDBMS 75
65 Formats: tabular RDBMS Row ID A1 1E0 22A 4A2 HBase 76
66 Formats: tabular 77
67 Formats: tabular 78
68 Formats: files (e.g., from HDFS) 79
69 Formats: files (e.g., from HDFS) Text 80
70 Formats: files (e.g., from HDFS) Text KeyValue 81
71 Formats: files (e.g., from HDFS) Text KeyValue SequenceFile 82
72 Text files Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed... 83
73 Text files Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed 84
74 Text files: NLine Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed... 85
75 Text files: NLine Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed 86
76 Key-Value Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed... 87
77 Key-Value Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed... Lorem sit consectetur adipiscing... ipsum dolor amet, elit, sed 88
78 Sequence files Hadoop binary format Stores generic key-s 89
79 Sequence files Hadoop binary format Stores generic key-s KeyLength Key ValueLength Value 90
80 Optimization 91
81 Optimization key type 1 -> type 1 Mapper Map Map Map Map Map Map key type A -> type A Reducer Reduce Reduce Reduce Reduce Reduce Reduce key type A -> type A 92
82 Optimization How to reduce* the amount of data key type shuffled 1 -> around? type 1 Mapper *pun intended (Eselsbrücke) Map Map Map Map Map Map key type A -> type A Reducer Reduce Reduce Reduce Reduce Reduce Reduce key type A -> type A 93
83 Optimization: Combine key type 1 -> type 1 Mapper Map Map Map Map Map Map key type A -> type A Combine Combine Combine Combine Combine Combine key type A -> type A Reducer Reduce Reduce Reduce Reduce Reduce Reduce key type A -> type A 94
84 Combine: the 90% case 95
85 Combine: the 90% case Often, the combine function is identical to the reduce function. Combine Reduce Disclaimer: there are assumptions 96
86 Combine=Reduce: Assumption 1 Key/Value types must be identical for reduce input and output. key type A -> type A Reduce Reduce Reduce Reduce Reduce Reduce key type A -> type A 97
87 Combine=Reduce : Assumption 2 98
88 Combine=Reduce : Assumption 2 Reduce function must be Commutative key A key A A B 99
89 Combine=Reduce : Assumption 2 Reduce function must be Commutative key A key A A B and Associative key A key A key A A B C 100
90 Optimization: Bring the Query to the Data Query Data 101
91 MapReduce: the APIs 102
92 Supported frameworks Hadoop MapReduce 103
93 Supported frameworks Hadoop MapReduce Java Streaming 104
94 Supported frameworks Hadoop MapReduce Java Streaming 105
95 Java API: Mapper import org.apache.hadoop.mapreduce.mapper; public class MyOwnMapper extends Mapper<K1, V1, K2, V2>{ } public void map(k1 key, V1, Context context) throws IOException, InterruptedException {... K2 new-key =... V2 new- =... context.write(new-key, new-);... } 106
96 Java API: Mapper import org.apache.hadoop.mapreduce.mapper; public class MyOwnMapper extends Mapper<K1, V1, K2, V2>{ } public void map(k1 key, V1, Context context) throws IOException, InterruptedException {... K2 new-key =... V2 new- =... context.write(new-key, new-);... } 107
97 Java API: Mapper import org.apache.hadoop.mapreduce.mapper; public class MyOwnMapper extends Mapper<K1, V1, K2, V2>{ } public void map(k1 key, V1, Context context) throws IOException, InterruptedException {... K2 new-key =... V2 new- =... context.write(new-key, new-);... } 108
98 Java API: Reducer import org.apache.hadoop.mapreduce.reducer; public class MyOwnReducer extends Reducer<K2, V2, K3, V3>{ } public void reduce (K2 key, Iterable<V2> s, Context context) throws IOException, InterruptedException {... K3 new-key =... V3 new- =... context.write(new-key, new-);... } 109
99 Java API: Reducer import org.apache.hadoop.mapreduce.reducer; public class MyOwnReducer extends Reducer<K2, V2, K3, V3>{ } public void reduce (K2 key, Iterable<V2> s, Context context) throws IOException, InterruptedException {... K3 new-key =... V3 new- =... context.write(new-key, new-);... } 110
100 Java API: Reducer import org.apache.hadoop.mapreduce.reducer; public class MyOwnReducer extends Reducer<K2, V2, K3, V3>{ } public void reduce (K2 key, Iterable<V2> s, Context context) throws IOException, InterruptedException {... K3 new-key =... V3 new- =... context.write(new-key, new-);... } 111
101 Java API: Job import org.apache.hadoop.mapreduce.job; public class MyMapReduceJob { public static void main(string[] args) throws Exception { Configuration conf = new Configuration(); Job job = Job.getInstance(conf, "word count"); job.setmapperclass(myownmapper.class); job.setreducerclass(myownreducer.class); FileInputFormat.addInputPath(job,...); FileOutputFormat.setOutputPath(job,...); } System.exit(job.waitForCompletion(true)? 0 : 1); 112
102 Java API: Job import org.apache.hadoop.mapreduce.job; public class MyMapReduceJob { public static void main(string[] args) throws Exception { Configuration conf = new Configuration(); Job job = Job.getInstance(conf, "word count"); job.setmapperclass(myownmapper.class); job.setreducerclass(myownreducer.class); FileInputFormat.addInputPath(job,...); FileOutputFormat.setOutputPath(job,...); } System.exit(job.waitForCompletion(true)? 0 : 1); 113
103 Java API: Job import org.apache.hadoop.mapreduce.job; public class MyMapReduceJob { public static void main(string[] args) throws Exception { Configuration conf = new Configuration(); Job job = Job.getInstance(conf, "word count"); job.setmapperclass(myownmapper.class); job.setreducerclass(myownreducer.class); FileInputFormat.addInputPath(job,...); FileOutputFormat.setOutputPath(job,...); } System.exit(job.waitForCompletion(true)? 0 : 1); 114
104 Java API: Job import org.apache.hadoop.mapreduce.job; public class MyMapReduceJob { public static void main(string[] args) throws Exception { Configuration conf = new Configuration(); Job job = Job.getInstance(conf, "word count"); job.setmapperclass(myownmapper.class); job.setreducerclass(myownreducer.class); FileInputFormat.addInputPath(job,...); FileOutputFormat.setOutputPath(job,...); } System.exit(job.waitForCompletion(true)? 0 : 1); 115
105 Java API: Job import org.apache.hadoop.mapreduce.job; public class MyMapReduceJob { public static void main(string[] args) throws Exception { Configuration conf = new Configuration(); Job job = Job.getInstance(conf, "word count"); job.setmapperclass(myownmapper.class); job.setreducerclass(myownreducer.class); FileInputFormat.addInputPath(job,...); FileOutputFormat.setOutputPath(job,...); } System.exit(job.waitForCompletion(true)? 0 : 1); 116
106 Java API: Combiner (=Reducer) import org.apache.hadoop.mapreduce.job; public class MyMapReduceJob { public static void main(string[] args) throws Exception { Configuration conf = new Configuration(); Job job = Job.getInstance(conf, "word count"); job.setmapperclass(myownmapper.class); job.setcombinerclass(myownreducer.class); job.setreducerclass(myownreducer.class); FileInputFormat.addInputPath(job,...); FileOutputFormat.setOutputPath(job,...); } System.exit(job.waitForCompletion(true)? 0 : 1); 117
107 Java API: InputFormat classes InputFormat 118
108 Java API: InputFormat classes InputFormat DBInputFormat RDBMS 119
109 Java API: InputFormat classes InputFormat DBInputFormat RDBMS TableInputFormat HBase 120
110 Java API: InputFormat classes InputFormat DBInputFormat TableInputFormat FileInputFormat RDBMS HBase 121
111 Java API: InputFormat classes InputFormat DBInputFormat RDBMS TableInputFormat HBase FileInputFormat KeyValueTextInputFormat Key file 122
112 Java API: InputFormat classes InputFormat DBInputFormat TableInputFormat FileInputFormat RDBMS HBase KeyValueTextInputFormat SequenceFileInputFormat Key file Sequence file 123
113 Java API: InputFormat classes InputFormat DBInputFormat TableInputFormat FileInputFormat RDBMS HBase KeyValueTextInputFormat SequenceFileInputFormat TextInputFormat Key file Sequence file 124
114 Java API: InputFormat classes InputFormat DBInputFormat TableInputFormat FileInputFormat RDBMS HBase KeyValueTextInputFormat SequenceFileInputFormat TextInputFormat FixedLengthInputFormat Key file Sequence file Text 125
115 Java API: InputFormat classes InputFormat DBInputFormat TableInputFormat FileInputFormat RDBMS HBase KeyValueTextInputFormat SequenceFileInputFormat TextInputFormat FixedLengthInputFormat NLineInputFormat Key file Sequence file Text 126
116 Java API: OutputFormat classes OutputFormat DBOutputFormat RDBMS TableOutputFormat HBase FileoutputFormat SequenceFileOutputFormat TextOutputFormat Text MapFileOutputFormat Sequence file 127
117 MapReduce: the physical layer 128
118 Possible storage layers Hadoop MapReduce 129
119 Possible storage layers Hadoop MapReduce Local Filesystem HDFS S3 Azure Blob Storage 130
120 Possible storage layers Hadoop MapReduce Local Filesystem HDFS S3 Azure Blob Storage 131
121 Hadoop MapReduce: Numbers Several TBs of data Data 132
122 Hadoop MapReduce: Numbers Several TBs of data Data MapMapMapMapMapMapMapMapMapMapMapMapMapMapMapMapMapMapMapMapMapMapMap Map Map Map Map Map Map Map Map Map Map Map Map Map Map Map Map Map Map Map Map Map Map Map Map M 1000s of nodes 133
123 Hadoop infrastructure (version 1) Namenode Datanode Datanode Datanode Datanode Datanode Datanode 134
124 Master-slave architecture Master Slave Slave Slave Slave Slave Slave 135
125 Hadoop infrastructure (version 1) Namenode + JobTracker Datanode + TaskTracker Datanode + TaskTracker Datanode + TaskTracker Datanode + TaskTracker Datanode + TaskTracker Datanode + TaskTracker 136
126 Hadoop infrastructure (version 1) Namenode + JobTracker Bring the Query to the Data Datanode + TaskTracker Datanode + TaskTracker Datanode + TaskTracker Datanode + TaskTracker Datanode + TaskTracker Datanode + TaskTracker 137
127 Tasks Task = or 138
128 Splits Map Map Map Map Map Map Map Map Reduce Reduce Reduce Reduce Reduce Reduce Reduce Reduce 139
129 Splits Map Map Map Map Map Map Map Map Reduce Reduce Reduce Reduce Reduce Reduce Reduce Reduce 140
130 Splits vs. map tasks Split 141
131 Splits vs. map tasks 1 split = 1 map task Split M M M M M 142
132 In practice M M M M 1 split Split 143
133 In practice M M M M 1 split = 1 block (subject to min and max size) Split Block 144
134 Splits vs. blocks: possible confusion Logical Level (MapReduce) Split Physical Level (HDFS) Block 145
135 Splits vs. blocks: possible confusion Logical Level (MapReduce) Split Record (key/ pair) Bit Physical Level (HDFS) Block 146
136 Records across blocks Logical Level (MapReduce) Split Physical Level (HDFS) Block 147
137 Records across blocks Logical Level (MapReduce) Split Remote read Physical Level (HDFS) Block 148
138 Fine-tuning to adjust splits to blocks Logical Level (MapReduce) Split Physical Level (HDFS) Block 149
139 Hadoop infrastructure (version 1) Namenode + JobTracker /dir/file Datanode + TaskTracker Datanode + TaskTracker Datanode + TaskTracker Datanode + TaskTracker Datanode + TaskTracker Datanode + TaskTracker 150
140 Hadoop infrastructure: map tasks Namenode + JobTracker As many map tasks as splits /dir/file M Datanode + TaskTracker M Datanode + TaskTracker M Datanode + TaskTracker Datanode + TaskTracker Datanode + TaskTracker M Datanode + TaskTracker 151
141 Hadoop infrastructure: map tasks As many map tasks as splits Namenode + JobTracker /dir/file Occasionally not possible to co-locate task and block M Datanode + TaskTracker M Datanode + TaskTracker M Datanode + TaskTracker Datanode + TaskTracker M Datanode + TaskTracker Datanode + TaskTracker 152
142 Hadoop infrastructure: reduce tasks A few reduce tasks Namenode + JobTracker /dir/file R R Datanode + TaskTracker Datanode + TaskTracker Datanode + TaskTracker Datanode + TaskTracker Datanode + TaskTracker Datanode + TaskTracker 153
143 Hadoop infrastructure: shuffling (inbetween) M R Namenode + JobTracker /dir/file M Datanode + TaskTracker R M Datanode + TaskTracker R M Datanode + TaskTracker Datanode + TaskTracker M Datanode + TaskTracker Datanode + TaskTracker 154
144 Shuffling phase Reducer Mappers 155
145 Shuffling phase Reducer Mappers Each mapper sorts its output key- pairs 156
146 Spilling to disk Key- pairs are spilled to disk if necessary 157
147 Shuffling phase Reducer Gets its key pairs over HTTP Mappers 158
148 Issue 1: Tight coupling Namenode + JobTracker Datanode + TaskTracker Datanode + TaskTracker Datanode + TaskTracker Datanode + TaskTracker Datanode + TaskTracker Datanode + TaskTracker 159
149 Issue 2: Scalability Namenode + JobTracker Only one! Datanode + TaskTracker Datanode + TaskTracker Datanode + TaskTracker Datanode + TaskTracker Datanode + TaskTracker Datanode + TaskTracker 160
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